Retweets of officials’ alarming vs reassuring messages during the COVID-19 pandemic: Implications for crisis management

Coronavirus related discussions have spiraled at an exponential rate since its initial outbreak. By the end of May, more than 6 million people were diagnosed with this infection. Twitter witnessed an outpouring of anxious tweets through messages associated with the spread of the virus. Government and health officials replied to the troubling tweets, reassuring the public with regular alerts on the virus's progress and information to defend against the virus. We observe that social media users are worried about Covid 19-related crisis and we identify three separate conversations on virus contagion, prevention, and the economy. We analyze the tone of officials’ tweet text as alarming and reassuring and capture the response of Twitter users to official communications. Such studies can provide insights to health officials and government agencies for crisis management, specifically regarding communicating emergency information to the public via social media for establishing reassurance.     

Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter

There is a strong interest among academics and practitioners in studying branding issues in the big data era. In this article, we examine the sentiments toward a brand, via brand authenticity, to identify the reasons for positive or negative sentiments on social media. Moreover, in order to increase precision, we investigate sentiment polarity on a five-point scale. From a database containing 2,282,912 English tweets with the keyword ‘Starbucks’, we use a set of 2204 coded tweets both for analyzing brand authenticity and sentiment polarity. First, we examine the tweets qualitatively to gain insights about brand authenticity sentiments. Then we analyze the data quantitatively to establish a framework in which we predict both the brand authenticity dimensions and their sentiment polarity. Through three qualitative studies, we discuss several tweets from the dataset that can be classified under the quality commitment, heritage, uniqueness, and symbolism categories. Using latent semantic analysis (LSA), we extract the common words in each category. We verify the robustness of previous findings with an in-lab experiment. Results from the support vector machine (SVM), as the quantitative research method, illustrate the effectiveness of the proposed procedure of brand authenticity sentiment analysis. It shows high accuracy for both the brand authenticity dimensions’ predictions and their sentiment polarity. We then discuss the theoretical and managerial implications of the studies.     

Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.     

Effect of penitence on social media trust and privacy concerns: The case of Facebook

Abuse of information entrusted to organizations can result in a variety of privacy violations and trust concerns for consumers. In the event of violations, a social media brand or organization renders an apology – a form of social account – to alleviate users’ concerns and maintain user membership and engagement with the platform. To explore the link between apology offered by a social media brand or organization and the users’ trust dynamics in the brand’s services, we study how organizational integrity can contribute to reducing individuals’ privacy concerns whiles increasing or repairing their trust. Drawing on organizational behavioral integrity literature, our proposed research model suggests that the persuasiveness of an apology following a data breach affects users’ trust or spillover trust through their perceptions of the degree of alignment between the words in the apology and the actions of the violating entity. Based on a survey of Facebook users, our findings show that persuasiveness of an apology has a significant impact on users’ perceptions of the alignment between the social media brand’s (i.e. Facebook) words and subsequent actions. These perceptions impact social media brand trust (i.e. users’ trust in Facebook and allied services such as Instagram). We also find that, post data breach incidence, while integrity of the social media organization partially mediates the relationship between persuasive apology and users’ trust, it fully mediates the relationship between the persuasive apology and the privacy concerns expressed by the users. However, users’ privacy concerns do not contribute much to the repair of trust needed to maintain their membership.     

African youths and the dangers of social networking: a culture-centered approach to using social media

With rising numbers of Facebook, Twitter and MXit users, Africa is increasingly gaining prominence in the sphere of social networking. Social media is increasingly becoming main stream; serving as important tools for facilitating interpersonal communication, business and educational activities. Qualitative analyses of relevant secondary data show that children and youths aged between 13 and 30 constitute Africa’s heaviest users of social media. Media reports have revealed cases of abuse on social media by youths. Social networks have severally been used as tools for perpetuating crimes such as; cyberbullying and violence against girls and women. This study proposes a ‘Culture-centered Approach’ to the use of social media in a bid to minimize these cybercrimes and encourage the responsible use of social media amongst African youths. The Culture-centered Approach, which incorporates the tenets of Information Ethics, stresses the need for the respect of the dignity and rights of other online users as well the application of good cultural values and ethical behavior while on social media platforms.     

Examining branding co-creation in brand communities on social media: Applying the paradigm of Stimulus-Organism-Response

This paper examines the antecedents of branding co-creation that include social networking sites’(SNSs) participation motivations,customer participation, brand trust and brand loyalty in social media brand communities by applying the “Stimulus-Organism-Response paradigm”. The survey method was used to gather data from 407 social media users. Data were analysed using structural equation modeling techniques. The findings reveal that SNSs’ participation motivations positively influence customer participation, which in turn significantly affects brand trust and brand loyalty. Consequently, both brand trust and brand loyalty positively influence branding co-creation in brand communities on social media. Furthermore, brand trust contributes as a mediator between customer participation and brand loyalty on social media brand communities. Although studies on relationships examined through the lens of the Stimulus-Organism-Response paradigm are popular, to the authors’ surprise there is scant literature examining the relationships between SNSs’ participation motivations, customer participation in social media brand communities, brand trust, brand loyalty and branding co-creation.     

The reflection of offline activities on users’ online social behavior: An observational study

The ever increasing presence of online social networks in users’ daily lives has led to the interplay between users’ online and offline activities. There have already been several works that have studied the impact of users’ online activities on their offline behavior, e.g., the impact of interaction with friends on an exercise social network on the number of daily steps. In this paper, we consider the inverse to what has already been studied and report on our extensive study that explores the potential causal effects of users’ offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. Our work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. We have collected data from two online social networks, namely Twitter and Foursquare, and systematically aligned user content on both social networks. On this basis, we have performed a natural experiment that took the form of an interrupted time series with a comparison group design to study whether users’ socially situated offline activities exhibited through their Foursquare check-ins impact their online behavior captured through the content they share on Twitter. Our main findings can be summarised as follows (1) a change in users’ offline behavior that affects the level of users’ exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting bars, can have a causal impact on users’ online topical interests and sentiment; and (2) the causal relations between users’ socially situated offline activities and their online social behavior can be used to build effective predictive models of users’ online topical interests and sentiments.     

Going beyond fact-checking to fight health misinformation: A multi-level analysis of the Twitter response to health news stories

Health misinformation has become an unfortunate truism of social media platforms, where lies could spread faster than truth. Despite considerable work devoted to suppressing fake news, health misinformation, including low-quality health news, persists and even increases in recent years. One promising approach to fighting bad information is studying the temporal and sentiment effects of health news stories and how they are discussed and disseminated on social media platforms like Twitter. As part of the effort of searching for innovative ways to fight health misinformation, this study analyzes a dataset of more than 1600 objectively and independently reviewed health news stories published over a 10-year span and nearly 50,000 Twitter posts responding to them. Specifically, it examines the source credibility of health news circulated on Twitter and the temporal, sentiment features of the tweets containing or responding to the health news reports. The results show that health news stories that are rated low by experts are discussed more, persist longer, and produce stronger sentiments than highly rated ones in the tweetosphere. However, the highly rated stories retained a fresh interest in the form of new tweets for a longer period. An in-depth understanding of the characteristics of health news distribution and discussion is the first step toward mitigating the surge of health misinformation. The findings provide insights into understanding the mechanism of health information dissemination on social media and practical implications to fight and mitigate health misinformation on digital media platforms.     

Using a Heuristic-Systematic Model to assess the Twitter user profile’s impact on disaster tweet credibility

Journalists, emergency responders, and the general public use Twitter during disasters as an effective means to disseminate emergency information. However, there is a growing concern about the credibility of disaster tweets. This concern negatively influences Twitter users’ decisions about whether to retweet information, which can delay the dissemination of accurate—and sometimes essential—communications during a crisis. Although verifying information credibility is often a time-consuming task requiring considerable cognitive effort, researchers have yet to explore how people manage this task while using Twitter during disaster situations.To address this, we adopt the Heuristic-Systematic Model of information processing to understand how Twitter users make retweet decisions by categorizing tweet content as systematically processed information and a Twitter user’s profile as heuristically processed information. We then empirically examine tweet content and Twitter user profiles, as well as how they interact to verify the credibility of tweets collected during two disaster events: the 2011 Queensland floods, and the 2013 Colorado floods. Our empirical results suggest that using a Twitter profile as source-credibility information makes it easier for Twitter users to assess the credibility of disaster tweets. Our study also reveals that the Twitter user profile is a reliable source of credibility information and enhances our understanding of timely communication on Twitter during disasters.     

Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage

Internet and social media offer firms novel ways of managing their marketing strategy and gain competitive advantage. The groups of users expressing themselves on the Internet about a particular topic, product, or brand are frequently called a virtual tribe or E-tribe. However, there are no automatic tools for identifying and studying the characteristics of these virtual tribes. Towards this aim, this paper presents Tribefinder, a system to reveal Twitter users’ tribal affiliations, by analyzing their tweets and language use. To show the potential of this instrument, we provide an example considering three specific tribal macro-categories: alternative realities, lifestyle, and recreation. In addition, we discuss the different characteristics of each identified tribe, in terms of use of language and social interaction metrics. Tribefinder illustrates the importance of adopting a new lens for studying virtual tribes, which is crucial for firms to properly design their marketing strategy, and for scholars to extend prior marketing research.     

Discovering socially similar users in social media datasets based on their socially important locations

Socially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations.     

Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media

When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order.     

Mining user interests over active topics on social networks

Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users.     

Social media overload,exhaustion, and use discontinuance: Examining the effects of information overload,system feature overload,and social overload

While users’ discontinuance of use has posed a challenge for social media in recent years, there is a paucity of knowledge on the relationships between different dimensions of overload and how overload adversely affects users’ social media discontinuance behaviors. To address this knowledge gap, this study employed the stressor–strain–outcome (SSO) framework to explain social media discontinuance behaviors from an overload perspective. It also conceptualized social media overload as a multidimensional construct consisting of system feature overload, information overload, and social overload. The proposed research model was empirically validated via 412 valid questionnaire responses collected from Facebook users. Our results indicated that the three types of overload are interconnected through system feature overload. System feature overload, information overload, and social overload engender user exhaustion, which in turn leads to users’ discontinued usage of social media. This study extends current technostress research by demonstrating the value of the SSO perspective in explaining users’ social media discontinuance.     

Neural opinion dynamics model for the prediction of user-level stance dynamics

Social media platforms allow users to express their opinions towards various topics online. Oftentimes, users’ opinions are not static, but might be changed over time due to the influences from their neighbors in social networks or updated based on arguments encountered that undermine their beliefs. In this paper, we propose to use a Recurrent Neural Network (RNN) to model each user’s posting behaviors on Twitter and incorporate their neighbors’ topic-associated context as attention signals using an attention mechanism for user-level stance prediction. Moreover, our proposed model operates in an online setting in that its parameters are continuously updated with the Twitter stream data and can be used to predict user’s topic-dependent stance. Detailed evaluation on two Twitter datasets, related to Brexit and US General Election, justifies the superior performance of our neural opinion dynamics model over both static and dynamic alternatives for user-level stance prediction.     

Multiplicity and dynamics of social representations of the COVID-19 pandemic on Chinese social media from 2019 to 2020

Documenting the emergent social representations of COVID-19 in public communication is necessary for critically reflecting on pandemic responses and providing guidance for global pandemic recovery policies and practices. This study documents the dynamics of changing social representations of the COVID-19 pandemic on one of the largest Chinese social media, Weibo, from December 2019 to April 2020. We draw on the social representation theory (SRT) and conceptualize topics and topic networks as a form of social representation. We analyzed a dataset of 40 million COVID-19 related posts from 9.7 million users (including the general public, opinion leaders, and organizations) using machine learning methods. We identified 12 topics and found an expansion in social representations of COVID-19 from a clinical and epidemiological perspective to a broader perspective that integrated personal illness experiences with economic and sociopolitical discourses. Discussions about COVID-19 science did not take a prominent position in the representations, suggesting a lack of effective science and risk communication. Further, we found the strongest association of social representations existed between the public and opinion leaders and the organizations’ representations did not align much with the other two groups, suggesting a lack of organizations’ influence in public representations of COVID-19 on social media in China.     

A network-based concept extraction for managing customer requests in a social media care context

Web 2.0 changed everyday life in many aspects, including the whole system that orbits around the purchase of products and services. This revolution necessarily involved also companies, because customers became increasingly demanding. The diffusion of social media platforms pushed customers to prefer this channel for quickly obtaining information and feedback about what they want to buy, as well as for asking help after the selling. In this framework, many organisations adopted a new way of providing assistance known as social customer care. A direct link to companies allows customers to obtain real-time solutions. In this paper, we introduce a new strategy for automatically managing the information listed in the requests that customers send to the social media accounts of companies. Our proposal relies on the use of network techniques for extracting high-level structures from texts, highlighting the different concepts expressed into the customers’ written requests. The texts can be then organised on the basis of this new emerging information. An application to the requests sent to the AppleSupport service on Twitter shows the effectiveness of the strategy.     

新媒体传播对转发意愿及品牌态度的影响研究

传统传播环境下企业营销传播活动对用户品牌态度形成具有显著影响,但社会化媒体的发展极大地改变了企业营销传播的生态环境,现阶段企业的社会化媒体传播并未获得预期影响力,需要从理论上对企业社会化媒体传播的策略及其影响因素进行创新性研究。本文采用实验研究方法,基于企业传播信息内容主题、信息源、传播策略与用户再传播意愿和品牌态度间关系的理论假设,实证研究发现:企业社会化媒体传播对用户品牌态度有正向显著影响;信息内容主题类型、信息源、传播组合策略对用户再传播意愿有显著影响;用户再传播意愿对用户品牌态度的影响不显著等。研究结论丰富了企业社会化媒体传播的理论研究,对企业社会化媒体传播实践具有指导意义。     

基于平台视角的政务社交媒体信息管理的问题与对策研究

[目的/意义]政务社交媒体是国家与社会重要的战略资源,它的有效归档管理,有助于推进开放政府建设。[方法/过程]本文以案例研究与文本分析的方法审查以微博、微信、今日头条为代表的社交媒体服务提供商的信息管理与条款。[结果/结论]通过研究,发现平台政策只提供有限的信息管理支持,对政务用户关注有限且专业性不足,造成政务社交媒体信息容易形成失存、失真、失控的风险。基于上述分析,本文提出平台可从如下方面构建对政务社交媒体信息的支持能力：关注与凸显政务类信息的特殊性、从用户视角完善平台政策、融合信息管理专业性要求。